Multiple Instance Learning. MIL is a popular method for weakly supervised learning. [rgb]0.,0,0In video-related tasks, MIL takes a video as a bag and clips in the video as instances [1]}, [2]}, [3]}. With a specific feature/score aggregation function, video-level labels can be used to indirectly supervise instance-level learning. The aggregation functions vary, max pooling[1]}, [5]}, [6]} and attention pooling[2]}, [3]}.
In this paper, we adopt a sparse continuous sampling strategy in our multiple instance pseudo label generator to
[rgb]0,0.,0 force the network to pay more attention to context around the most anomalous part.
